Overview

Dataset statistics

Number of variables15
Number of observations35
Missing cells6
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory123.7 B

Variable types

Numeric13
Categorical1
Text1

Alerts

Año is highly overall correlated with ADSL and 6 other fieldsHigh correlation
ADSL is highly overall correlated with Año and 6 other fieldsHigh correlation
Porcentaje 1 is highly overall correlated with Año and 7 other fieldsHigh correlation
Cablemodem is highly overall correlated with Año and 6 other fieldsHigh correlation
Porcentaje 2 is highly overall correlated with Porcentaje 6High correlation
Fibra óptica is highly overall correlated with Año and 7 other fieldsHigh correlation
Porcentaje 3 is highly overall correlated with Porcentaje 1 and 2 other fieldsHigh correlation
Wireless is highly overall correlated with Año and 6 other fieldsHigh correlation
Porcentaje 4 is highly overall correlated with Porcentaje 6High correlation
Otros is highly overall correlated with Año and 7 other fieldsHigh correlation
Total is highly overall correlated with Año and 6 other fieldsHigh correlation
Porcentaje 6 is highly overall correlated with Porcentaje 2 and 1 other fieldsHigh correlation
Porcentaje 1 has 1 (2.9%) missing valuesMissing
Porcentaje 2 has 1 (2.9%) missing valuesMissing
Porcentaje 3 has 1 (2.9%) missing valuesMissing
Porcentaje 4 has 1 (2.9%) missing valuesMissing
Porcentaje 5 has 1 (2.9%) missing valuesMissing
Porcentaje 6 has 1 (2.9%) missing valuesMissing
ADSL has unique valuesUnique
Cablemodem has unique valuesUnique
Fibra óptica has unique valuesUnique
Wireless has unique valuesUnique
Otros has unique valuesUnique
Total has unique valuesUnique
Periodo has unique valuesUnique

Reproduction

Analysis started2023-07-09 02:16:26.908966
Analysis finished2023-07-09 02:16:58.601865
Duration31.69 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Año
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.8857
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2023-07-08T21:16:58.710684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5641353
Coefficient of variation (CV)0.0012707039
Kurtosis-1.2023376
Mean2017.8857
Median Absolute Deviation (MAD)2
Skewness0.023491055
Sum70626
Variance6.5747899
MonotonicityDecreasing
2023-07-08T21:16:58.838025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 4
11.4%
2020 4
11.4%
2019 4
11.4%
2018 4
11.4%
2017 4
11.4%
2016 4
11.4%
2015 4
11.4%
2014 4
11.4%
2022 3
8.6%
ValueCountFrequency (%)
2014 4
11.4%
2015 4
11.4%
2016 4
11.4%
2017 4
11.4%
2018 4
11.4%
2019 4
11.4%
2020 4
11.4%
2021 4
11.4%
2022 3
8.6%
ValueCountFrequency (%)
2022 3
8.6%
2021 4
11.4%
2020 4
11.4%
2019 4
11.4%
2018 4
11.4%
2017 4
11.4%
2016 4
11.4%
2015 4
11.4%
2014 4
11.4%

Trimestre
Categorical

Distinct4
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size408.0 B
3
2
1
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row1
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 9
25.7%
2 9
25.7%
1 9
25.7%
4 8
22.9%

Length

2023-07-08T21:16:59.055237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T21:16:59.349863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 9
25.7%
2 9
25.7%
1 9
25.7%
4 8
22.9%

Most occurring characters

ValueCountFrequency (%)
3 9
25.7%
2 9
25.7%
1 9
25.7%
4 8
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 9
25.7%
2 9
25.7%
1 9
25.7%
4 8
22.9%

Most occurring scripts

ValueCountFrequency (%)
Common 35
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 9
25.7%
2 9
25.7%
1 9
25.7%
4 8
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 9
25.7%
2 9
25.7%
1 9
25.7%
4 8
22.9%

ADSL
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3040425.5
Minimum1395277
Maximum3803024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2023-07-08T21:16:59.544031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1395277
5-th percentile1513767.9
Q12279711
median3557216
Q33723156
95-th percentile3789835.1
Maximum3803024
Range2407747
Interquartile range (IQR)1443445

Descriptive statistics

Standard deviation835412.18
Coefficient of variation (CV)0.27476818
Kurtosis-1.0555899
Mean3040425.5
Median Absolute Deviation (MAD)231480
Skewness-0.73203155
Sum1.0641489 × 108
Variance6.9791351 × 1011
MonotonicityNot monotonic
2023-07-08T21:16:59.759658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1395277 1
 
2.9%
3792493 1
 
2.9%
3622575 1
 
2.9%
3723518 1
 
2.9%
3708898 1
 
2.9%
3722794 1
 
2.9%
3776442 1
 
2.9%
3782085 1
 
2.9%
3803024 1
 
2.9%
3574294 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1395277 1
2.9%
1468333 1
2.9%
1533240 1
2.9%
1657615 1
2.9%
1950631 1
2.9%
2018587 1
2.9%
2175211 1
2.9%
2213949 1
2.9%
2263889 1
2.9%
2295533 1
2.9%
ValueCountFrequency (%)
3803024 1
2.9%
3792493 1
2.9%
3788696 1
2.9%
3782085 1
2.9%
3776442 1
2.9%
3767821 1
2.9%
3764038 1
2.9%
3756153 1
2.9%
3723518 1
2.9%
3722794 1
2.9%

Porcentaje 1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)97.1%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean-2.735
Minimum-17.02
Maximum1.33
Zeros0
Zeros (%)0.0%
Negative27
Negative (%)77.1%
Memory size408.0 B
2023-07-08T21:16:59.979368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-17.02
5-th percentile-10.132
Q1-4.4625
median-1.22
Q3-0.18
95-th percentile0.446
Maximum1.33
Range18.35
Interquartile range (IQR)4.2825

Descriptive statistics

Standard deviation4.1300497
Coefficient of variation (CV)-1.510073
Kurtosis5.0383113
Mean-2.735
Median Absolute Deviation (MAD)1.51
Skewness-2.1332405
Sum-92.99
Variance17.057311
MonotonicityNot monotonic
2023-07-08T21:17:00.248240image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
-0.28 2
 
5.7%
-0.27 1
 
2.9%
-1.06 1
 
2.9%
-2.71 1
 
2.9%
0.39 1
 
2.9%
-0.37 1
 
2.9%
-1.42 1
 
2.9%
-0.15 1
 
2.9%
-4.98 1
 
2.9%
-4.23 1
 
2.9%
Other values (23) 23
65.7%
ValueCountFrequency (%)
-17.02 1
2.9%
-15.02 1
2.9%
-7.5 1
2.9%
-7.2 1
2.9%
-5.89 1
2.9%
-5.46 1
2.9%
-4.98 1
2.9%
-4.77 1
2.9%
-4.54 1
2.9%
-4.23 1
2.9%
ValueCountFrequency (%)
1.33 1
2.9%
0.55 1
2.9%
0.39 1
2.9%
0.38 1
2.9%
0.32 1
2.9%
0.31 1
2.9%
0.16 1
2.9%
-0.15 1
2.9%
-0.17 1
2.9%
-0.21 1
2.9%

Cablemodem
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4122601.1
Minimum2407330
Maximum6073426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2023-07-08T21:17:00.488980image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2407330
5-th percentile2513854.3
Q12966749
median4038141
Q35132233
95-th percentile5998559
Maximum6073426
Range3666096
Interquartile range (IQR)2165484

Descriptive statistics

Standard deviation1225276.7
Coefficient of variation (CV)0.29720962
Kurtosis-1.3803127
Mean4122601.1
Median Absolute Deviation (MAD)1139915
Skewness0.16985543
Sum1.4429104 × 108
Variance1.501303 × 1012
MonotonicityNot monotonic
2023-07-08T21:17:00.729232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
6031970 1
 
2.9%
2806359 1
 
2.9%
3670221 1
 
2.9%
3383434 1
 
2.9%
3276251 1
 
2.9%
3210602 1
 
2.9%
3124855 1
 
2.9%
3035272 1
 
2.9%
2898226 1
 
2.9%
3981129 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
2407330 1
2.9%
2461670 1
2.9%
2536219 1
2.9%
2569868 1
2.9%
2668248 1
2.9%
2756294 1
2.9%
2806359 1
2.9%
2840203 1
2.9%
2898226 1
2.9%
3035272 1
2.9%
ValueCountFrequency (%)
6073426 1
2.9%
6031970 1
2.9%
5984240 1
2.9%
5979214 1
2.9%
5826257 1
2.9%
5641731 1
2.9%
5424782 1
2.9%
5371824 1
2.9%
5259351 1
2.9%
5005115 1
2.9%

Porcentaje 2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)94.1%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean2.7647059
Minimum-3.17
Maximum8.48
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)8.6%
Memory size408.0 B
2023-07-08T21:17:00.904724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-3.17
5-th percentile-1.394
Q11.76
median2.78
Q34.0225
95-th percentile6.2425
Maximum8.48
Range11.65
Interquartile range (IQR)2.2625

Descriptive statistics

Standard deviation2.3405778
Coefficient of variation (CV)0.84659197
Kurtosis1.4952514
Mean2.7647059
Median Absolute Deviation (MAD)1.235
Skewness0.02237454
Sum94
Variance5.4783045
MonotonicityNot monotonic
2023-07-08T21:17:01.076468image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3.27 2
 
5.7%
2.04 2
 
5.7%
0.88 1
 
2.9%
-3.17 1
 
2.9%
4.03 1
 
2.9%
8.48 1
 
2.9%
2.74 1
 
2.9%
2.95 1
 
2.9%
8.16 1
 
2.9%
3.04 1
 
2.9%
Other values (22) 22
62.9%
ValueCountFrequency (%)
-3.17 1
2.9%
-1.55 1
2.9%
-1.31 1
2.9%
0.41 1
2.9%
0.88 1
2.9%
0.99 1
2.9%
1.43 1
2.9%
1.49 1
2.9%
1.75 1
2.9%
1.79 1
2.9%
ValueCountFrequency (%)
8.48 1
2.9%
8.16 1
2.9%
5.21 1
2.9%
5.08 1
2.9%
4.95 1
2.9%
4.51 1
2.9%
4.4 1
2.9%
4.27 1
2.9%
4.03 1
2.9%
4 1
2.9%

Fibra óptica
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean739062.74
Minimum139187
Maximum2871541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2023-07-08T21:17:01.302078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum139187
5-th percentile149586.3
Q1167988
median226285
Q31138802
95-th percentile2370658.6
Maximum2871541
Range2732354
Interquartile range (IQR)970814

Descriptive statistics

Standard deviation786177.18
Coefficient of variation (CV)1.0637489
Kurtosis1.048522
Mean739062.74
Median Absolute Deviation (MAD)76922
Skewness1.3634799
Sum25867196
Variance6.1807456 × 1011
MonotonicityNot monotonic
2023-07-08T21:17:01.442677image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2871541 1
 
2.9%
164371 1
 
2.9%
193964 1
 
2.9%
180777 1
 
2.9%
180146 1
 
2.9%
178070 1
 
2.9%
169898 1
 
2.9%
167788 1
 
2.9%
139187 1
 
2.9%
217460 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
139187 1
2.9%
149363 1
2.9%
149682 1
2.9%
150323 1
2.9%
150839 1
2.9%
155494 1
2.9%
162663 1
2.9%
164371 1
2.9%
167788 1
2.9%
168188 1
2.9%
ValueCountFrequency (%)
2871541 1
2.9%
2723285 1
2.9%
2219533 1
2.9%
2072236 1
2.9%
1566048 1
2.9%
1472246 1
2.9%
1362976 1
2.9%
1311199 1
2.9%
1170879 1
2.9%
1106725 1
2.9%

Porcentaje 3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)100.0%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean9.9714706
Minimum-14.43
Maximum64.2
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)11.4%
Memory size408.0 B
2023-07-08T21:17:01.592460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-14.43
5-th percentile-6.043
Q13.125
median6.74
Q311.815
95-th percentile40.909
Maximum64.2
Range78.63
Interquartile range (IQR)8.69

Descriptive statistics

Standard deviation15.4389
Coefficient of variation (CV)1.5483073
Kurtosis5.9035601
Mean9.9714706
Median Absolute Deviation (MAD)4.62
Skewness2.1551758
Sum339.03
Variance238.35964
MonotonicityNot monotonic
2023-07-08T21:17:01.735601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5.44 1
 
2.9%
18.09 1
 
2.9%
7.29 1
 
2.9%
0.35 1
 
2.9%
1.17 1
 
2.9%
4.81 1
 
2.9%
1.26 1
 
2.9%
2.08 1
 
2.9%
-14.43 1
 
2.9%
2.85 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
-14.43 1
2.9%
-10.32 1
2.9%
-3.74 1
2.9%
-0.64 1
2.9%
0.35 1
2.9%
1.17 1
2.9%
1.26 1
2.9%
2.08 1
2.9%
2.85 1
2.9%
3.95 1
2.9%
ValueCountFrequency (%)
64.2 1
2.9%
56.86 1
2.9%
32.32 1
2.9%
22.7 1
2.9%
19.42 1
2.9%
18.09 1
2.9%
12.36 1
2.9%
12.02 1
2.9%
11.98 1
2.9%
11.32 1
2.9%

Wireless
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248280.03
Minimum70749
Maximum557110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2023-07-08T21:17:01.884368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum70749
5-th percentile75610.3
Q185107.5
median194267
Q3394963
95-th percentile548942.7
Maximum557110
Range486361
Interquartile range (IQR)309855.5

Descriptive statistics

Standard deviation174354.29
Coefficient of variation (CV)0.70224856
Kurtosis-1.2843068
Mean248280.03
Median Absolute Deviation (MAD)113760
Skewness0.49571479
Sum8689801
Variance3.0399419 × 1010
MonotonicityNot monotonic
2023-07-08T21:17:02.030271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
557110 1
 
2.9%
85370 1
 
2.9%
106443 1
 
2.9%
85813 1
 
2.9%
84813 1
 
2.9%
85119 1
 
2.9%
81455 1
 
2.9%
85452 1
 
2.9%
85726 1
 
2.9%
165300 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
70749 1
2.9%
72405 1
2.9%
76984 1
2.9%
79098 1
2.9%
81455 1
2.9%
82077 1
2.9%
84530 1
2.9%
84813 1
2.9%
85096 1
2.9%
85119 1
2.9%
ValueCountFrequency (%)
557110 1
2.9%
556243 1
2.9%
545814 1
2.9%
523107 1
2.9%
492415 1
2.9%
476968 1
2.9%
434548 1
2.9%
421554 1
2.9%
413259 1
2.9%
376667 1
2.9%

Porcentaje 4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)100.0%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean6.6764706
Minimum-9.53
Maximum46.35
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)11.4%
Memory size408.0 B
2023-07-08T21:17:02.169275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-9.53
5-th percentile-1.911
Q11.535
median3.675
Q39.1225
95-th percentile24.978
Maximum46.35
Range55.88
Interquartile range (IQR)7.5875

Descriptive statistics

Standard deviation10.13092
Coefficient of variation (CV)1.5174065
Kurtosis6.5687946
Mean6.6764706
Median Absolute Deviation (MAD)3.175
Skewness2.17922
Sum227
Variance102.63554
MonotonicityNot monotonic
2023-07-08T21:17:02.305313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.16 1
 
2.9%
-0.42 1
 
2.9%
24.04 1
 
2.9%
1.18 1
 
2.9%
-0.36 1
 
2.9%
4.5 1
 
2.9%
-4.68 1
 
2.9%
0.1 1
 
2.9%
1.41 1
 
2.9%
6.11 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
-9.53 1
2.9%
-4.68 1
2.9%
-0.42 1
2.9%
-0.36 1
2.9%
0.1 1
2.9%
0.16 1
2.9%
0.56 1
2.9%
1.18 1
2.9%
1.41 1
2.9%
1.91 1
2.9%
ValueCountFrequency (%)
46.35 1
2.9%
26.72 1
2.9%
24.04 1
2.9%
17.53 1
2.9%
17.52 1
2.9%
11.43 1
2.9%
9.81 1
2.9%
9.76 1
2.9%
9.71 1
2.9%
7.36 1
2.9%

Otros
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147431.09
Minimum54300
Maximum265328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2023-07-08T21:17:02.455876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum54300
5-th percentile55548.6
Q162763.5
median100554
Q3247918.5
95-th percentile256784.2
Maximum265328
Range211028
Interquartile range (IQR)185155

Descriptive statistics

Standard deviation85868.717
Coefficient of variation (CV)0.58243291
Kurtosis-1.8373973
Mean147431.09
Median Absolute Deviation (MAD)45466
Skewness0.22508143
Sum5160088
Variance7.3734365 × 109
MonotonicityNot monotonic
2023-07-08T21:17:02.596903image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
235230 1
 
2.9%
58763 1
 
2.9%
131740 1
 
2.9%
56122 1
 
2.9%
55746 1
 
2.9%
55088 1
 
2.9%
54300 1
 
2.9%
59482 1
 
2.9%
58668 1
 
2.9%
98870 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
54300 1
2.9%
55088 1
2.9%
55746 1
2.9%
56122 1
2.9%
58668 1
2.9%
58763 1
2.9%
58976 1
2.9%
59157 1
2.9%
59482 1
2.9%
66045 1
2.9%
ValueCountFrequency (%)
265328 1
2.9%
264326 1
2.9%
253552 1
2.9%
253036 1
2.9%
252596 1
2.9%
251996 1
2.9%
250455 1
2.9%
250190 1
2.9%
248821 1
2.9%
247016 1
2.9%

Porcentaje 5
Real number (ℝ)

MISSING 

Distinct34
Distinct (%)100.0%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean6.3035294
Minimum-23.67
Maximum134.74
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)54.3%
Memory size408.0 B
2023-07-08T21:17:02.876579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-23.67
5-th percentile-13.5345
Q1-2.67
median-0.345
Q31.235
95-th percentile70.198
Maximum134.74
Range158.41
Interquartile range (IQR)3.905

Descriptive statistics

Standard deviation30.22079
Coefficient of variation (CV)4.794265
Kurtosis11.13431
Mean6.3035294
Median Absolute Deviation (MAD)1.7
Skewness3.2577246
Sum214.32
Variance913.29617
MonotonicityNot monotonic
2023-07-08T21:17:03.128368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1.56 1
 
2.9%
0.16 1
 
2.9%
134.74 1
 
2.9%
0.67 1
 
2.9%
1.19 1
 
2.9%
1.45 1
 
2.9%
-8.71 1
 
2.9%
1.22 1
 
2.9%
-0.52 1
 
2.9%
-1.67 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
-23.67 1
2.9%
-19.3 1
2.9%
-10.43 1
2.9%
-8.71 1
2.9%
-8.09 1
2.9%
-7.72 1
2.9%
-6.78 1
2.9%
-4.38 1
2.9%
-2.91 1
2.9%
-1.95 1
2.9%
ValueCountFrequency (%)
134.74 1
2.9%
90.14 1
2.9%
59.46 1
2.9%
18.11 1
2.9%
2.18 1
2.9%
1.69 1
2.9%
1.56 1
2.9%
1.45 1
2.9%
1.24 1
2.9%
1.22 1
2.9%

Total
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8297800.5
Minimum6398398
Maximum11091128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2023-07-08T21:17:03.385611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum6398398
5-th percentile6556030.1
Q17057455
median8110444
Q39260441.5
95-th percentile10724411
Maximum11091128
Range4692730
Interquartile range (IQR)2202986.5

Descriptive statistics

Standard deviation1399611.3
Coefficient of variation (CV)0.16867256
Kurtosis-0.94187945
Mean8297800.5
Median Absolute Deviation (MAD)1125613
Skewness0.42457074
Sum2.9042302 × 108
Variance1.9589117 × 1012
MonotonicityNot monotonic
2023-07-08T21:17:03.579618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
11091128 1
 
2.9%
6907356 1
 
2.9%
7724943 1
 
2.9%
7429664 1
 
2.9%
7305854 1
 
2.9%
7251673 1
 
2.9%
7206950 1
 
2.9%
7130079 1
 
2.9%
6984831 1
 
2.9%
8037053 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
6398398 1
2.9%
6464468 1
2.9%
6595271 1
2.9%
6598496 1
2.9%
6737732 1
2.9%
6816188 1
2.9%
6907356 1
2.9%
6935068 1
2.9%
6984831 1
2.9%
7130079 1
2.9%
ValueCountFrequency (%)
11091128 1
2.9%
10958684 1
2.9%
10624009 1
2.9%
10489794 1
2.9%
10085541 1
2.9%
9863084 1
2.9%
9647972 1
2.9%
9571562 1
2.9%
9356199 1
2.9%
9164684 1
2.9%

Porcentaje 6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)100.0%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1.6423529
Minimum-4.05
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)5.7%
Memory size408.0 B
2023-07-08T21:17:03.758911image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-4.05
5-th percentile-0.356
Q10.94
median1.81
Q32.295
95-th percentile3.736
Maximum4.01
Range8.06
Interquartile range (IQR)1.355

Descriptive statistics

Standard deviation1.5369589
Coefficient of variation (CV)0.93582743
Kurtosis4.7010182
Mean1.6423529
Median Absolute Deviation (MAD)0.755
Skewness-1.4548277
Sum55.84
Variance2.3622428
MonotonicityNot monotonic
2023-07-08T21:17:03.910747image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1.21 1
 
2.9%
-1.11 1
 
2.9%
3.97 1
 
2.9%
1.69 1
 
2.9%
0.75 1
 
2.9%
0.62 1
 
2.9%
1.08 1
 
2.9%
3.22 1
 
2.9%
0.72 1
 
2.9%
2.12 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
-4.05 1
2.9%
-1.11 1
2.9%
0.05 1
2.9%
0.22 1
2.9%
0.62 1
2.9%
0.72 1
2.9%
0.75 1
2.9%
0.8 1
2.9%
0.91 1
2.9%
1.03 1
2.9%
ValueCountFrequency (%)
4.01 1
2.9%
3.97 1
2.9%
3.61 1
2.9%
3.6 1
2.9%
3.22 1
2.9%
3.15 1
2.9%
2.92 1
2.9%
2.48 1
2.9%
2.3 1
2.9%
2.28 1
2.9%

Periodo
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size408.0 B
2023-07-08T21:17:04.272425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.257143
Min length12

Characters and Unicode

Total characters429
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st rowJul-Sept 2022
2nd rowAbr-Jun 2022
3rd rowEne-Mar 2022
4th rowOct-Dic 2021
5th rowJul-Sept 2021
ValueCountFrequency (%)
jul-sept 9
12.9%
ene-mar 9
12.9%
abr-jun 9
12.9%
oct-dic 8
11.4%
2016 4
 
5.7%
2017 4
 
5.7%
2015 4
 
5.7%
2018 4
 
5.7%
2014 4
 
5.7%
2020 4
 
5.7%
Other values (3) 11
15.7%
2023-07-08T21:17:05.087037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 49
 
11.4%
0 39
 
9.1%
- 35
 
8.2%
35
 
8.2%
1 28
 
6.5%
J 18
 
4.2%
u 18
 
4.2%
e 18
 
4.2%
n 18
 
4.2%
r 18
 
4.2%
Other values (19) 153
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149
34.7%
Decimal Number 140
32.6%
Uppercase Letter 70
16.3%
Dash Punctuation 35
 
8.2%
Space Separator 35
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 18
12.1%
e 18
12.1%
n 18
12.1%
r 18
12.1%
t 17
11.4%
c 16
10.7%
b 9
6.0%
a 9
6.0%
p 9
6.0%
l 9
6.0%
Decimal Number
ValueCountFrequency (%)
2 49
35.0%
0 39
27.9%
1 28
20.0%
6 4
 
2.9%
7 4
 
2.9%
5 4
 
2.9%
8 4
 
2.9%
4 4
 
2.9%
9 4
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
J 18
25.7%
A 9
12.9%
M 9
12.9%
E 9
12.9%
S 9
12.9%
O 8
11.4%
D 8
11.4%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Space Separator
ValueCountFrequency (%)
35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 219
51.0%
Common 210
49.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 18
 
8.2%
u 18
 
8.2%
e 18
 
8.2%
n 18
 
8.2%
r 18
 
8.2%
t 17
 
7.8%
c 16
 
7.3%
b 9
 
4.1%
A 9
 
4.1%
a 9
 
4.1%
Other values (8) 69
31.5%
Common
ValueCountFrequency (%)
2 49
23.3%
0 39
18.6%
- 35
16.7%
35
16.7%
1 28
13.3%
6 4
 
1.9%
7 4
 
1.9%
5 4
 
1.9%
8 4
 
1.9%
4 4
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 429
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49
 
11.4%
0 39
 
9.1%
- 35
 
8.2%
35
 
8.2%
1 28
 
6.5%
J 18
 
4.2%
u 18
 
4.2%
e 18
 
4.2%
n 18
 
4.2%
r 18
 
4.2%
Other values (19) 153
35.7%

Interactions

2023-07-08T21:16:54.948606image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:27.487159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:30.039266image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:32.050023image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:34.316140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:36.715901image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:39.091738image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:41.498768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:43.749289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:45.852515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:48.496036image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:50.480597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:52.732598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:55.163777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:27.673064image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:30.194998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:32.217096image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:34.471759image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:36.902081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:39.277251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:41.719697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:43.889450image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:46.035600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:48.697882image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:50.673233image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:52.946010image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:55.324960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:27.896305image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:30.318275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:32.391687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:34.650425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:37.045459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:39.478842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:41.858190image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:44.064045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:46.168202image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:48.828206image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:50.810878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:53.099477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:55.488305image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:28.078589image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:30.468807image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:32.616536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:34.880404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:37.199210image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:39.710690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:41.991686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:44.202539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:46.373992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:48.986361image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:51.001970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:53.256552image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:55.774135image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:28.247842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:30.603570image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:32.785785image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:35.021698image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:37.336816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:39.890024image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:42.229863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:44.351834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:46.534165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:49.135656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:51.210659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:53.448827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:55.949449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:28.440883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:30.747005image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:32.961860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:35.161549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:37.522930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:40.067382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:42.385417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:44.486809image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:46.981655image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:49.280723image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:51.345797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:53.618723image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:56.150723image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:28.622074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:30.908954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:33.126015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:35.381012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:37.681929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:40.248680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:42.529030image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:44.664896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:47.119901image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:49.425989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:51.539835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:53.797458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:56.336542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:28.835347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:31.122122image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:33.290092image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:35.541929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:37.858472image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:40.378102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:42.706719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:44.879700image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:47.277925image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:49.567738image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:51.772729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:54.000403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:56.523093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:29.063637image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:31.304021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:33.456609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:35.709391image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:38.009823image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:40.574887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:42.898851image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:45.054266image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:47.418148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:49.699358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:51.945225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:54.165215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:56.695366image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:29.195991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:31.449525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:33.642625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:35.855600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:38.217329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:40.751949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:43.060786image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:45.214766image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:47.608420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:49.919976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:52.110142image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:54.294647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:56.876359image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:29.384869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:31.582743image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:33.771861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:36.205917image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:38.473644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:40.925745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:43.233285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:45.359725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:47.822802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:50.041603image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:52.258821image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:54.470054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:57.274725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:29.659484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:31.741922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:33.969126image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:36.387811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:38.696454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:41.121699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:43.442604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:45.532807image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:48.023256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:50.191962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:52.422835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:54.645233image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:57.462225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:29.833507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:31.902831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:34.141893image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:36.559013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:38.868687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:41.307824image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:43.590560image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:45.706131image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:48.267499image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:50.327219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:52.563308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-08T21:16:54.763521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-08T21:17:05.302581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
AñoADSLPorcentaje 1CablemodemPorcentaje 2Fibra ópticaPorcentaje 3WirelessPorcentaje 4OtrosPorcentaje 5TotalPorcentaje 6Trimestre
Año1.000-0.917-0.7880.991-0.2500.9860.4560.9730.1830.7930.1300.9880.3390.000
ADSL-0.9171.0000.779-0.9170.217-0.943-0.441-0.906-0.283-0.877-0.108-0.915-0.3560.000
Porcentaje 1-0.7880.7791.000-0.7990.150-0.804-0.675-0.779-0.289-0.713-0.102-0.787-0.2240.223
Cablemodem0.991-0.917-0.7991.000-0.2230.9840.4510.9810.2170.8010.1500.9950.3560.000
Porcentaje 2-0.2500.2170.150-0.2231.000-0.206-0.010-0.2310.491-0.0280.008-0.2100.6390.000
Fibra óptica0.986-0.943-0.8040.984-0.2061.0000.5030.9610.2200.8120.1290.9810.3760.000
Porcentaje 30.456-0.441-0.6750.451-0.0100.5031.0000.4680.3330.5260.0380.4590.3820.000
Wireless0.973-0.906-0.7790.981-0.2310.9610.4681.0000.2410.8170.1490.9770.3700.000
Porcentaje 40.183-0.283-0.2890.2170.4910.2200.3330.2411.0000.4120.0260.2190.5010.000
Otros0.793-0.877-0.7130.801-0.0280.8120.5260.8170.4121.0000.2190.8120.4840.000
Porcentaje 50.130-0.108-0.1020.1500.0080.1290.0380.1490.0260.2191.0000.1680.2580.000
Total0.988-0.915-0.7870.995-0.2100.9810.4590.9770.2190.8120.1681.0000.3850.000
Porcentaje 60.339-0.356-0.2240.3560.6390.3760.3820.3700.5010.4840.2580.3851.0000.098
Trimestre0.0000.0000.2230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0981.000

Missing values

2023-07-08T21:16:57.702962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T21:16:58.205867image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T21:16:58.484346image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AñoTrimestreADSLPorcentaje 1CablemodemPorcentaje 2Fibra ópticaPorcentaje 3WirelessPorcentaje 4OtrosPorcentaje 5TotalPorcentaje 6Periodo
0202231395277-4.9860319700.8828715415.445571100.162352301.56110911281.21Jul-Sept 2022
1202221468333-4.235979214-1.55272328522.705562431.91231609-8.09109586843.15Abr-Jun 2022
2202211533240-7.5060734261.4922195337.115458144.34251996-0.24106240091.28Ene-Mar 2022
3202141657615-15.0259842402.71207223632.325231076.232525960.96104897944.01Oct-Dic 2021
4202131950631-3.3758262573.2715660486.374924153.24250190-1.33100855412.26Jul-Sept 2021
5202122018587-7.2056417314.0014722468.024769689.762535521.2498630842.23Abr-Jun 2021
6202112175211-1.7554247820.9913629763.954345483.08250455-1.0296479720.80Ene-Mar 2021
7202042213949-2.2153718242.14131119911.984215542.012530361.6995715622.30Oct-Dic 2020
8202032263889-1.3852593515.0811708795.804132599.712488210.7393561993.60Jul-Sept 2020
9202022295533-0.1750051152.0711067255.623766676.9124701618.1190310562.48Abr-Jun 2020
AñoTrimestreADSLPorcentaje 1CablemodemPorcentaje 2Fibra ópticaPorcentaje 3WirelessPorcentaje 4OtrosPorcentaje 5TotalPorcentaje 6Periodo
25201623782085-0.2730352728.161677882.08854520.10594821.2271300793.22Abr-Jun 2016
26201613792493-0.282806359-3.1716437118.0985370-0.42587630.166907356-1.11Ene-Mar 2016
272015438030240.3828982262.04139187-14.43857261.4158668-0.5269848310.72Oct-Dic 2015
282015337886960.5528402033.041626637.84845302.9958976-0.3169350681.74Jul-Sept 2015
292015237678210.3127562943.30150839-10.32820773.7759157-10.4368161881.16Abr-Jun 2015
30201513756153-0.2126682485.2116818812.36790982.7566045-7.7267377322.11Ene-Mar 2015
312014437640381.332536219-1.31149682-3.7476984-9.53715732.1865984960.05Oct-Dic 2014
322014337147640.1625698684.401554944.108509617.5370049-2.9165952712.02Jul-Sept 2014
332014237088820.3224616702.26149363-0.64724052.3472148-1.0764644681.03Abr-Jun 2014
34201413697066NaN2407330NaN150323NaN70749NaN72930NaN6398398NaNEne-Mar 2014